Hierarchical search strategy for the efficient detection of gravitational waves from non-precessing coalescing compact binaries with aligned-spins

TL;DR

This paper introduces a two-stage hierarchical search strategy for detecting gravitational waves from non-precessing coalescing compact binaries with aligned spins, significantly reducing computational costs and enabling broader parameter space exploration.

Contribution

The paper presents a novel hierarchical search method integrated into the PyCBC pipeline, improving computational efficiency for gravitational wave detection in larger parameter spaces.

Findings

Achieved ~20x computational gain in simulated Gaussian noise data

Expected up to a few times computational savings in real detector data

Enables search for precessing binaries by freeing computational resources

Abstract

In the first two years of Gravitational Wave (GW) Astronomy, half a dozen compact binary coalescences (CBCs) have been detected. As the sensitivities and bandwidths of the detectors improve and new detectors join the network, many more sources are expected to be detected. The goal will not only be to find as many sources as possible in the data but to understand the dynamics of the sources much more precisely. Standard searches are currently restricted to a smaller parameter space which assumes aligned spins. Construction of a larger and denser parameter space, and optimising the resultant increase in false alarms, pose a serious computational challenge. We present here a two-stage hierarchical strategy to search for CBCs in data from a network of detectors and demonstrate the computational advantage in real life scenario by introducing it in the standard {\tt PyCBC} pipeline with the usual restricted parameter space. With this strategy, in simulated data containing stationary Gaussian noise, we obtain a computational gain of $\sim 20$ over the flat search. In real data, we expect the computational gain up to a factor of few. This saving in the computational effort will, in turn, allow us to search for precessing binaries. Freeing up computation time for the regular analyses will provide more options to search for sources of different kinds and to fulfil the never-ending urge for extracting more science out of the data with limited resources.